Predicting high impact academic papers using citation network features

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    13 Citations (Scopus)

    Abstract

    Predicting future high impact academic papers is of benefit to a range of stakeholders, including governments, universities, academics, and investors. Being able to predict 'the next big thing' allows the allocation of resources to fields where these rapid developments are occurring. This paper develops a new method for predicting a paper's future impact using features of the paper's neighbourhood in the citation network, including measures of interdisciplinarity. Predictors of high impact papers include high early citation counts of the paper, high citation counts by the paper, citations of and by highly cited papers, and interdisciplinary citations of the paper and of papers that cite it. The Scopus database, consisting of over 24 million publication records from 1996-2010 across a wide range of disciplines, is used to motivate and evaluate the methods presented.

    Original languageEnglish
    Title of host publicationTrends and Applications in Knowledge Discovery and Data Mining - PAKDD 2013 International Workshops
    Subtitle of host publicationDMApps, DANTH, QIMIE, BDM, CDA, CloudSD, Revised Selected Papers
    Pages14-25
    Number of pages12
    DOIs
    Publication statusPublished - 2013
    Event17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013 - Gold Coast, QLD, Australia
    Duration: 14 Apr 201317 Apr 2013

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume7867 LNAI
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013
    Country/TerritoryAustralia
    CityGold Coast, QLD
    Period14/04/1317/04/13

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